Reliability analysis of unsaturated soil slope stability using spatial random field-based Bayesian method
Rainfall and water level change are two of the main factors causing failure of reservoir slopes. Thus, soil-fluid mechanics is applied with the fluctuation of groundwater table. However, many of the geotechnical parameters needed for the analysis are highly varied. Spatial random field-based Bayesia...
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Published in | Landslides Vol. 18; no. 3; pp. 1177 - 1189 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.03.2021
Springer Nature B.V |
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Abstract | Rainfall and water level change are two of the main factors causing failure of reservoir slopes. Thus, soil-fluid mechanics is applied with the fluctuation of groundwater table. However, many of the geotechnical parameters needed for the analysis are highly varied. Spatial random field-based Bayesian method is proposed, which can systematically assimilate prior knowledge, borehole testing data, and long-term monitoring data to obtain posterior distributions. There are three major components of the method. They include unsaturated soil-fluid coupling mechanics, spatial multivariate discretization, and subset Monte Carlo simulation of reliability analysis. The approach is applied to the Shiliushubao slope of the Three Gorges Reservoir area, which is located 9.0 km downstream of the Badong County in Hubei Province, China. Results prove that the updated key geotechnical parameters will quantitatively predict the geohazards of landslide, especially for unsaturated soil slope conditions that suffer an unprecedented heavy rainfall subject to low reservoir water level in the upcoming summer. |
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AbstractList | Rainfall and water level change are two of the main factors causing failure of reservoir slopes. Thus, soil-fluid mechanics is applied with the fluctuation of groundwater table. However, many of the geotechnical parameters needed for the analysis are highly varied. Spatial random field-based Bayesian method is proposed, which can systematically assimilate prior knowledge, borehole testing data, and long-term monitoring data to obtain posterior distributions. There are three major components of the method. They include unsaturated soil-fluid coupling mechanics, spatial multivariate discretization, and subset Monte Carlo simulation of reliability analysis. The approach is applied to the Shiliushubao slope of the Three Gorges Reservoir area, which is located 9.0 km downstream of the Badong County in Hubei Province, China. Results prove that the updated key geotechnical parameters will quantitatively predict the geohazards of landslide, especially for unsaturated soil slope conditions that suffer an unprecedented heavy rainfall subject to low reservoir water level in the upcoming summer. |
Author | Huang, M. L. Sun, D. A. Wang, C. H. Keleta, Y. |
Author_xml | – sequence: 1 givenname: M. L. surname: Huang fullname: Huang, M. L. organization: Department of Civil Engineering, Shanghai University – sequence: 2 givenname: D. A. surname: Sun fullname: Sun, D. A. organization: Department of Civil Engineering, Shanghai University – sequence: 3 givenname: C. H. surname: Wang fullname: Wang, C. H. email: ch_wang@shu.edu.cn organization: Department of Civil Engineering, Shanghai University – sequence: 4 givenname: Y. surname: Keleta fullname: Keleta, Y. organization: School of Advanced Studies, University of Phoenix |
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CitedBy_id | crossref_primary_10_1016_j_jrmge_2024_02_008 crossref_primary_10_1061_AJRUA6_RUENG_1317 crossref_primary_10_3390_geotechnics4020021 crossref_primary_10_1007_s10064_021_02364_6 crossref_primary_10_1016_j_soildyn_2022_107632 crossref_primary_10_1016_j_trgeo_2025_101527 crossref_primary_10_1007_s11069_023_05947_6 crossref_primary_10_1080_19475705_2024_2398111 crossref_primary_10_1007_s10346_022_02023_1 crossref_primary_10_1007_s10706_024_02936_9 crossref_primary_10_1144_qjegh2024_138 crossref_primary_10_1007_s10346_024_02250_8 crossref_primary_10_1061_IJGNAI_GMENG_9445 crossref_primary_10_3799_dqkx_2022_361 crossref_primary_10_1002_nag_3712 crossref_primary_10_1016_j_compgeo_2024_106199 crossref_primary_10_1007_s10064_024_03799_3 crossref_primary_10_1007_s10346_025_02486_y crossref_primary_10_1080_17499518_2024_2443462 crossref_primary_10_1080_17499518_2023_2278136 crossref_primary_10_1007_s10346_021_01812_4 crossref_primary_10_1016_j_compgeo_2024_106920 crossref_primary_10_1016_j_enggeo_2023_107356 crossref_primary_10_1016_j_compgeo_2024_106798 |
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Keywords | Geotechnical parameters Reliability analysis Unsaturated soil slope Spatial random fields Bayesian method |
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SubjectTerms | Agriculture Bayesian analysis Boreholes Canyons Civil Engineering Downstream effects Earth and Environmental Science Earth Sciences Fields (mathematics) Fluid mechanics Geography Geological hazards Groundwater Groundwater levels Groundwater table Heavy rainfall Landslides Monte Carlo simulation Natural Hazards Parameters Probability theory Rain Rainfall Reliability Reliability analysis Reservoir water Reservoirs Slope stability Soil analysis Soil conditions Soil mechanics Soil stability Stability analysis Statistical methods Technical Note Unsaturated soils Water level fluctuations Water levels Water table |
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Title | Reliability analysis of unsaturated soil slope stability using spatial random field-based Bayesian method |
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